c461a835f9
I’ve released a new beta version of rusqlite_migration today and I’ve renamed the `async-tokio-rusqlite` feature that you use to `alpha-async-tokio-rusqlite`. I suggest updating dependencies as done in this PR. By the way, if you would like to share feedback on the async APIs, feel free to use this conversation: https://github.com/cljoly/rusqlite_migration/discussions/127 Reading the release notes from [tokio-rusqlite][1], the main breaking change is to the method `Connection::call`: > - **breaking:** `Connection::call` now takes `tokio_rusqlite::Result` instead > of `rusqlite::Result`. My understanding is that we can either deal with a `Result<Result<…>>` or call `Connection::call_unwrap`, which could panic if the connection is closed. I’ve opted for using only `Connection::call` and, effectively, flattening the `Result<Result<…>>`, to limit the potential for panics. [1]: https://github.com/programatik29/tokio-rusqlite/blob/master/CHANGELOG.md#050-25-nov-2023 |
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README.md
🐾 Tabby
Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features:
- Self-contained, with no need for a DBMS or cloud service.
- OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE).
- Supports consumer-grade GPUs.
🔥 What's New
- 11/27/2023 v0.6.0 released!
- 10/24/2023 ⛳️ Major updates for Tabby IDE plugins across VSCode/Vim/IntelliJ!
- 10/15/2023 RAG-based code completion is enabled by detail in v0.3.0🎉! Check out the blogpost explaining how Tabby utilizes repo-level context to get even smarter!
Archived
- 11/09/2023 v0.5.5 released! With a redesign of UI + performance improvement.
- 10/04/2023 Check out the model directory for the latest models supported by Tabby.
- 09/18/2023 Apple's M1/M2 Metal inference support has landed in v0.1.1!
- 08/31/2023 Tabby's first stable release v0.0.1 🥳.
- 08/28/2023 Experimental support for the CodeLlama 7B.
- 08/24/2023 Tabby is now on JetBrains Marketplace!
👋 Getting Started
You can find our documentation here.
Run Tabby in 1 Minute
The easiest way to start a Tabby server is by using the following Docker command:
docker run -it \
--gpus all -p 8080:8080 -v $HOME/.tabby:/data \
tabbyml/tabby \
serve --model TabbyML/StarCoder-1B --device cuda
For additional options (e.g inference type, parallelism), please refer to the documentation page.
🤝 Contributing
Get the Code
git clone --recurse-submodules https://github.com/TabbyML/tabby
cd tabby
If you have already cloned the repository, you could run the git submodule update --recursive --init command to fetch all submodules.
Build
-
Set up the Rust environment by following this tutorial.
-
Install the required dependencies:
# For MacOS
brew install protobuf
# For Ubuntu / Debian
apt-get install protobuf-compiler libopenblas-dev
- Now, you can build Tabby by running the command
cargo build.
Start Hacking!
... and don't forget to submit a Pull Request
🌍 Community
- #️⃣ Slack - connect with the TabbyML community
- 🎤 Twitter / X - engage with TabbyML for all things possible
- 📚 LinkedIn - follow for the latest from the community
- 💌 Newsletter - subscribe to unlock Tabby insights and secrets